Utilize this practical and easy-to-follow guide to modernize traditional enterprise data warehouse and business intelligence environments with next-generation big data technologies. Next-Generation Big Data takes a holistic approach, covering the most important aspects of modern enterprise big data. The book covers not only the main technology stack but also the next-generation tools and applications used for big data warehousing, data warehouse optimization, real-time and batch data ingestion and processing, real-time data visualization, big data governance, data wrangling, big data cloud deployments, and distributed in-memory big data computing. Finally, the book has an extensive and detailed coverage of big data case studies from Navistar, Cerner, British Telecom, Shopzilla, Thomson Reuters, and Mastercard. What You’ll Learn Install Apache Kudu, Impala, and Spark to modernize enterprise data warehouse and business intelligence environments, complete with real-world, easy-to-follow examples, and practical advice Integrate HBase, Solr, Oracle, SQL Server, MySQL, Flume, Kafka, HDFS, and Amazon S3 with Apache Kudu, Impala, and Spark Use StreamSets, Talend, Pentaho, and CDAP for real-time and batch data ingestion and processing Utilize Trifacta, Alteryx, and Datameer for data wrangling and interactive data processing Turbocharge Spark with Alluxio, a distributed in-memory storage platform Deploy big data in the cloud using Cloudera Director Perform real-time data visualization and time series analysis using Zoomdata, Apache Kudu, Impala, and Spark Understand enterprise big data topics such as big data governance, metadata management, data lineage, impact analysis, and policy enforcement, and how to use Cloudera Navigator to perform common data governance tasks Implement big data use cases such as big data warehousing, data warehouse optimization, Internet of Things, real-time data ingestion and analytics, complex event processing, and scalable predictive modeling Study real-world big data case studies from innovative companies, including Navistar, Cerner, British Telecom, Shopzilla, Thomson Reuters, and Mastercard Who This Book Is For BI and big data warehouse professionals interested in gaining practical and real-world insight into next-generation big data processing and analytics using Apache Kudu, Impala, and Spark; and those who want to learn more about other advanced enterprise topics

One viable option for organizations looking to harness massive amounts of data is the data lake, a single repository for storing all the raw data, both structured and unstructured, that floods into the company. But that isn’t the end of the story. The key to making a data lake work is data governance, using metadata to provide valuable context through tagging and cataloging. This practical report examines why metadata is essential for managing, migrating, accessing, and deploying any big data solution. Authors Federico Castanedo and Scott Gidley dive into the specifics of analyzing metadata for keeping track of your data—where it comes from, where it’s located, and how it’s being used—so you can provide safeguards and reduce risk. In the process, you’ll learn about methods for automating metadata capture. This report also explains the main features of a data lake architecture, and discusses the pros and cons of several data lake management solutions that support metadata. These solutions include: Traditional data integration/management vendors such as the IBM Research Accelerated Discovery Lab Tooling from open source projects, including Teradata Kylo and Informatica Startups such as Trifacta and Zaloni that provide best of breed technology